Recent designs of lower-limb prostheses for amputee patients improve stability and may also provide a decrease in energy consumption in level ground walking. In addition, advances in computerized control and powered-prosthesis design have further improved the function of artificial legs which now can assist users with versatile activities beyond level walking. However, without knowing user movement intent, current artificial limbs cannot properly select the correct control mode to adjust the joint impedance or drive powered joint motion.
Current computerized prosthetic legs allow the user to change the movement mode manually. Such voluntary prosthesis control is cumbersome and does not allow smooth task transition. Therefore, neural control of computerized, powered prosthetic legs would provide an important advance in the art.
Surface electromyographic (EMG) signals represent neuromuscular control information of the user and are readily accessible. EMG signals are one of the major neural control sources for powered upper limb prostheses, experimental motorized orthoses, and rehabilitation robots. Although myoelectric control and advanced EMG pattern recognition (PR)-based control have been used in upper-limb prostheses, no EMG-controlled lower-limb prostheses are available.
While studies have attempted to use EMG signals to identify locomotion modes for prosthetic leg control, they lack systematic experimental design and study methodology. For example, Peeraer et al. (L. Peeraer, B. Aeyels, and G. Van der Perre, “Development of EMG-based mode and intent recognition algorithms for a computer-controlled above-knee prosthesis,” J Biomed Eng, vol. 12, pp. 178-82, 1990) demonstrated the difference in the EMG signal envelope among level ground walking and ascending and descending a ramp and concluded that EMG signals from hip muscles might be used to classify these locomotion modes. However, no method or apparatus for neural control of computerized, powered prosthetic legs was described or suggested.
Jin et al. (D. Jin, J. Yang, R. Zhang, R. Wang, and J. Zhang, “Terrain Identification for Prosthetic Knees Based on Electromyographic Signal Features,” Tsinghua Science and Technology, vol. 11, pp. 74-9, 2006) developed an algorithm for terrain identification during walking. The features were extracted from EMG signals from a complete stride cycle. Using such features, the algorithm only made one decision per stride cycle, resulting in a full stride cycle time delay (generally over 1 second) in real-time. In practical application, this is inadequate for safe prosthetic control. A strategy for effective neural control of computerized, powered prosthetic legs must include means for generating timely updates of the user's lower-limb movement modes.
One of the challenges in using EMG signals from leg muscles to classify user's movement modes in a timely manner is that the recorded EMG signals are time-varying. The features of gait EMG signals generally show large variation within the same task mode (class), which might result in overlaps of features among classes and therefore low accuracy for pattern recognition. Work in upper-limb prosthesis control, however, has suggested EMG signals, though time-varying, have a relatively small change in signal content within the short time duration of 200 ms.
Another challenge is that the accuracy of EMG PR might be inadequate for robust prosthesis control in patients with high-level amputations because insufficient neural content is available in EMG signals from only the residual limb. However, the neural control information for the amputated limb remains in the patient's residual nerves.
To allow access to these neural control commands via EMG signals, a novel neural-machine interface (NMI) technology called targeted muscle reinnervation (TMR) has been developed and successfully applied on patients with transhumeral or higher amputations for improved myoelectric artificial arm control. (See T. A. Kuiken, L. A. Miller, R. D. Lipschutz, B. A. Lock, K. Stubblefield, P. D. Marasco, P. Zhou, and G. A. Dumanian, “Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study,” Lancet, vol. 369, pp. 371-80, 2007; and T. A. Kuiken, G. A. Dumanian, R. D. Lipschutz, L. A. Miller, and K. A. Stubblefield, “The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee,” Prosthet Orthot Int, vol. 28, pp. 245-53, 2004, which are incorporated by reference for their description of TMR).
In TMR surgery, the residual nerves are transferred to alternative residual muscles that are no longer biomechanically functional. After successful motor reinnervation, the voluntary neural control signals propagate along the efferent nerves and activate the residual muscles. The resultant EMG signals, containing neural control information for a missing arm and hand, can control an artificial elbow, wrist, and even thumb/fingers via an EMG PR method. (See P. Zhou, M. M. Lowery, K. B. Englehart, H. Huang, G. Li, L. Hargrove, J. P. Dewald, and T. A. Kuiken, “Decoding a New Neural-Machine Interface for Control of Artificial Limbs,” J Neurophysiol, 2007; and H. Huang, P. Zhou, G. Li, and T. A. Kuiken, “An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface,” IEEE Trans Neural Syst Rehabil Eng, vol. 16, pp. 37-45, 2008, which are incorporated by reference).
If TMR could be used for leg prosthesis control, it could provide control information for the lower leg and foot in the form of EMG signals from reinnervated muscle. However, much must be done to implement a TMR based system for neural control of computerized, powered prosthetic legs before clinical applications can be implemented.